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Sebastian Bader | Semantic Technologies for Assisted Decision-Making in Industrial Maintenance

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Sebastian Bader | Semantic Technologies for Assisted Decision-Making in Industrial Maintenance

  1. 1. KIT – University of the State of Baden-Württemberg and National Laboratory of the Helmholtz Association KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI) www.kit.edu Semantic Technologies for Assisted Decision-Making in Industrial Maintenance Sebastian Bader Research Associate
  2. 2. Institut KSRI9/29/20162 Sebastian Bader sebastian.bader@kit.edu Predictive Maintenance • Forecasting break-down probabilities Condition-Based Maintenance • Discover failure patterns Preventive Maintenance • Specified service intervals Reactive Maintenance • Run to failure Industrial Maintenance Process ! Amount of unplanned downtimes
  3. 3. Institut KSRI Improvement Areas 9/29/20163 Sebastian Bader sebastian.bader@kit.edu Dispatcher Client Technician Machine Remote support Schedule Tour Local/global planning Real-time tour optimization Predictive Maintenance Information provision Semi-automated decision making
  4. 4. Institut KSRI Next Generation of Maintenance  Reduction of unplanned downtimes  Less travel time for field technicians by tour optimization  Improved planning of resources and capacities  Automated/Supported decision making where possible  Automatic data exchange with customers/suppliers  Integrating external services and competences  Provisioning of contextualized information 9/29/20164 Sebastian Bader sebastian.bader@kit.edu
  5. 5. Institut KSRI Challenges  How can advanced data insights be used to create business value?  How can available data contribute to a more efficient maintenance process?  What are the current limitations and how can we solve them? 9/29/20165 Sebastian Bader sebastian.bader@kit.edu
  6. 6. Institut KSRI  Predictive Analytics provides flexibility…  … to prepare resources  … to organize technicians  … to adjust capacities and demands  Data-driven approaches reduce complexity…  … by regarding all side effects  … by suggesting appropriate actions  … by supplying related information Transforming Predictive Analytics into Business Value 9/29/20166 Sebastian Bader sebastian.bader@kit.edu Dispatcher SchedulePredictive Maintenance Capacity Demand Predictions at its own are not sufficient, only the ability to react provides value! Reducing uncertainty increases efficiency: Therefore, an integrated support system for the whole process is necessary.
  7. 7. Institut KSRI System Integration via Semantic Web Technologies  Current systems already solve some challenges  forecasting machine downtimes  optimized scheduling of technicians  real-time tour planning  Need for addressing constantly added/removed resources  New machine instances, types, technologies  New customers, departments, partners  Disconnected machines, expiring contracts  Need for system integration across departments, organizations, and countries  Need for flexible, modularized and decentralized integration approach 9/29/20167 Sebastian Bader sebastian.bader@kit.edu Tour SchedulePredictive Maintenance
  8. 8. Institut KSRI Data Model: the Maintenance Ontology 9/29/20168 Sebastian Bader sebastian.bader@kit.edu
  9. 9. Institut KSRI System Integration via Semantic Web Technologies How to enable the integration of external services with potentially unknown requirements, heterogeneous access methods and varying data formats into a decentralized network?  Smart Web Services1 (SmartWS)  Encapsulate context-based decision logic  Lifting and lowering to agreed data format according to Linked Data Principles  Access via HTTP and REST  Self-describing and therefore automatically controllable  Consumer and producer at the same time (=Prosumers) 9/29/20169 Sebastian Bader sebastian.bader@kit.edu Tour SchedulePredictive Maintenance System 1 System 2 HTTP REST RDF Wrapper library Wrapper library Lifting Lowering JSON mapping mapping Output Functionality Input Provenance 2 Maleshkova, Maria, et al. "Smart Web Services (SmartWS)–The Future of Services on the Web." The IPSI BgD Transactions on Advanced Research: 15.
  10. 10. Institut KSRI9/29/201610 Sebastian Bader sebastian.bader@kit.edu Reusable SmartWS Data Sources, Devices, Sensors, Wearables, Algorithms, etc. Composite Applications SmartWS Devices SmartWS Sensors SmartWS Algorithms SmartWSSmartWS SmartWS Execution Engine Reference SmartWS Architecture
  11. 11. Institut KSRI Web Services and Linked Data Platform  Access to data  Stored, managed and published through DBs  Linked Data Platform2 for reading/writing RDF  RESTful methods for data requesting and manipulation  SmartWS provide Linked APIs with semantic descriptions  Requesting Web services  WSDL/SOAP or RESTful communication 9/29/201611 Sebastian Bader sebastian.bader@kit.edu Consistent handling of data and services 2 Speicher, Steve, John Arwe, and Ashok Malhotra. "Linked data platform 1.0." W3C Recommendation, February 26 (2015).
  12. 12. Institut KSRI Provision of Contextualized Information  Identify topics and context  Reports, manuals, posts  Understand the current situation  Dynamic information from heterogeneous input channels  Static knowledge on processes and resources  Modeling information objects as resources, enhanced with meta data, in a common manner 9/29/201612 Sebastian Bader sebastian.bader@kit.edu Technician Machine History Task Situation
  13. 13. Institut KSRI Social Maintenance Network  “There must be someone who knows the solution to my problem. How can I find him? How can I access his expertise?”  Implicit knowledge not queryable  Segregation by organizational unit, language, region, … 1. Connect people depending on qualification, experience, task, and availability 2. Supply available information where needed  Solution: Social network for fast and reliable communication and adaptive information provision 9/29/201613 Sebastian Bader sebastian.bader@kit.edu Dispatcher Technician
  14. 14. Institut KSRI9/29/201614 Sebastian Bader sebastian.bader@kit.edu  Platform for information and knowledge exchange based on Linked Data representations Semantic Media Wiki Semantic MediaWiki = 𝑾𝒊𝒌𝒊𝒑𝒆𝒅𝒊𝒂 + 𝑺𝒆𝒎𝒂𝒏𝒕𝒊𝒄 𝑴𝒆𝒕𝒉𝒐𝒅𝒔 • Collaborative work • Sharing knowledge • Easy syntax • Browser-based (stationary and mobile) • Perfect integration with semantic technologies • Access on data views (near real-time) • OLAP functionality • Extendable platform
  15. 15. Institut KSRI Semantic Text Analysis and Similarity Matching From Semantic Media Wiki to Social Platform 9/29/201615 Sebastian Bader sebastian.bader@kit.edu Task Route Chat Help Tools Mobile application Task X Machine Y Task-related information views Activity 1 Activity 2 Task A Problem P ID: 0053A435-ZD Changing air filter of AC unit Type: Cutter Installed: 2011 Color: green Location: Tech Inc. Configuration: DFR-24 Mario Rossi John Doe Max MustermannJean Untel Community support Chat functionality Procedure: 1.Open shell 2.Check power supply 3.Change fuse 4.Start test sequence 5.Check power LED 6.Detach wires 7.Lift filter 8.Insert new filter 9.Attach wires 10.Restart test sequence 11.Fill report 12.Let customer sign 13.Close shell 14.Start machine History: Oil pressure error Vibrations Regular maintenance Installation Client: Name: Tech Inc. Contact: Peter Müller Tel. no.: 01234 555 Time: 9:00 to 11:30 Address: IoT Road 1 Smallville
  16. 16. Institut KSRI9/29/201616 Sebastian Bader sebastian.bader@kit.edu MAINTENANCE SCENARIO BUSINESS MODELS CUSTOMER (LEASING) Leasing inclusive repair commitment MANUFACTURER F CUSTOMER (MACHINE OWNER) Full-Service-Contract MANUFACTURER/MAINTAINER PLATFORM @ SENSOR DATA (periodic intervals) BREAKDOWNPREDECTION BREAKDOWNPREDECTION ;ANALYTIC RESULTS component breakdown probability etc. SENSOR DATA measurements, conditions etc. 2 PREDICTIVE ANALYTICS measurements, conditions etc. IMPROVEMENTS INCREASING EFFICIENCY Shorter maintenance and travel times INCREASING AVAILABILITY Minimizing unexpected breakdowns MINIMIZING MAINTENANCE COSTS Reduced investigation time MAXIMIZING TOTAL LIFETIME Optimized maintenance 3 @
  17. 17. Institut KSRI Future Business Cases  Full-Service Contracts  Automated maintenance organization allows efficient risk management  Machine-as-a-Service instead of single sales event  Strategic skill management  Integrated modules enable the detection of missing/required skills of work force  Combination of operational planning with strategic simulations lead to fact-based decisions  Externalization of low profit tasks  Marketplace for external maintenance provider  Gradual access to sensitive technical information 9/29/201617 Sebastian Bader sebastian.bader@kit.edu
  18. 18. Institut KSRI Conclusion  Semantic Web Technologies enable a flexible and decentralized integration of heterogeneous resources.  Consistent data modeling with RDF for a system-wide information access  Smart Web Services encapsulate automated decision logic in order to reduce complexity and increase processing speed  Semantic annotations of documents, situations, and employees allow context-related information provision  Semantic Technologies enable more efficient industrial maintenance processes with new business models 9/29/201618 Sebastian Bader sebastian.bader@kit.edu
  19. 19. Institut KSRI Acknowledgements This work is partially supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) as part of the “Smart Service Welt” program under grant number 01 MD16015 B (STEP) 9/29/201619 Sebastian Bader sebastian.bader@kit.edu

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